Update README.md

README.md

@@ -98,7 +98,7 @@ These data points should ***not***  be classified as technical outliers because
 
 ---
 
-Part 3: Embeddings
+# Part 3: Embeddings
 We have selected three distinct Transformer models to evaluate the trade-off between semantic understanding and computational efficiency for our recipe recommendation engine:
 
 **sentence-transformers/all-MiniLM-L6-v2** (The Baseline): Chosen for its extreme speed and compact size (80MB). It represents the industry standard for lightweight CPU-based inference, serving as our baseline for "maximum efficiency."
@@ -127,4 +127,14 @@ We selected **BAAI/bge-small-en-v1.5** as the optimal embedding model for our re
 
 * **Performance:** Crucially, it achieved the **highest similarity score** in our evaluation, demonstrating superior semantic understanding compared to the faster but less accurate `all-MiniLM-L6-v2`.
 * **Efficiency:** It matched the precision of the resource-heavy `all-mpnet-base-v2` (which requires 420 MB) while maintaining a significantly lighter footprint.
-* **Conclusion:** This specific balance allows our system to deliver the most relevant recipe recommendations without compromising on computational efficiency.
+* **Conclusion:** This specific balance allows our system to deliver the most relevant recipe recommendations without compromising on computational efficiency.
+
+# Part 4- IO Pipeline:
+on our first try, we tried to use an OCR model:TrOCRProcessor
+it wan't successfull, the model couln't predict some handwritten recipes, and sometimes even hulucinated.
+
+we decided to try using: Qwen2.5-VL Vision-Language Model and the results were much better! 
+Comparison:
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/6910977ace661438b728d763/tSsR5JNuGO_yNBRCxoH3s.png)
+